PRIMO.ai

Overview

• How do I leverage AI?
• Courses
• Reading Material

Background

• What is AI?
• History of AI
• Current State

AI Breakthroughs

• Case Studies
• Capabilities
• AI Patents after 2013

AI Fun

• Google AI Experiments
• TensorFlow Playground
• TensorFlow.js Demos

How to...

• AI Solver
• Strategy & Tactics
• Checklists

Forward Thinking

• Moonshots
• Journey to Singularity
• Creatives

Algorithms

• About Algorithms & Neural Network Models

Supervised

Labeled (desired solution) data is fed into the algorithm. The training data set has inputs as well as the desired output. During the training session, the model will adjust its variables to map inputs to the corresponding output.

• ...predict values
  • Linear Regression
  • Bayesian Linear Regression
  • Support Vector Regression (SVR)
  • Ordinal Regression
  • Poisson Regression
  • Tree-based...
    • Fast Forest Quantile Regression
    • Decision Forest Regression
  • Boosted Decision Tree Regression
  • General Regression Neural Network (GRNN)

• Anomaly Detection ...find outliers
  • One-class Support Vector Machine (SVM)
  • Principal Components Analysis (PCA)-based Anomaly Detection

• Classification ...predict categories
  • Perceptron (P)
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (KNN)
  • Logistic Regression
  • Naive Bayes
  • Tree-based...
    • Boosted Decision Tree
    • Random Forest (or) Random Decision Forest
    • Decision Jungle
  • Feed Forward Neural Network (FF or FFNN)
  • Artificial Neural Network (ANN)
  • Deep Neural Network (DNN)

• Other
  • Hopfield Network (HN)
  • Energy-based Model (EBN)
  • Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)

Convolutional

• (Deep) Convolutional Neural Network (DCNN/CNN)
• (Deep) Residual Network (DRN) - ResNet

Deonvolutional

• Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)

Sequence

• Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
• Attention Model
• Sequence to Sequence (Seq2Seq)
• (Tree) Recursive Neural (Tensor) Network (RNTN)
• Neural Turing Machine

Unsupervised

There is not a target outcome. The algorithms will cluster the data set for different groups. Some uses of Unsupervised Learning are (1) data compression, (2) classification, (3) clustering, and (4) outlier detection

Unsupervised: Probabilistic/Generative

A probability distribution over a set of classes for each input sample. Unlabeled data is classified as (1) conditional probability of the target Y, or (2) conditional probability of the observable X given a target Y

• Clustering
  • K-Means
  • Mean-Shift Clustering
  • Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
  • Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

• Hierarchical to include clustering
  • Hierarchical Cluster Analysis (HCA)
  • Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
  • Hierarchical Temporal Memory (HTM)

• Other
  • Restricted Boltzmann Machine (RBM)
  • Deep Belief Network (DBN)
  • Variational Autoencoder (VAE)

Competitive

• Generative Adversarial Network (GAN)
• Kohonen Network (KN)/Self Organizing Maps (SOM)

Unsupervised: Non-Probabilistic

Unlabeled data is fed into the algorithm with the algorithm seperating the feature space and return the class associated with the space where a sample originates from.

• Autoencoder (AE) / Encoder-Decoder
• (Stacked) Denoising Autoencoder (DAE)
• Sparse Autoencoder (SAE)

Semi-Supervised

• Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
• Context-Conditional Generative Adversarial Network (CC-GAN)

Reinforcement (RL)

An 'agent' algorithm receives a delayed reward in the next time step to evaluate its previous action. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. In combination with Neural Networks it is capable of solving more complex tasks.

• Markov Decision Process (MDP)
• Deep Reinforcement Learning (DRL)
• Deep Q Learning (DQN)
• Neural Coreference
• State-Action-Reward-State-Action (SARSA)
• Deep Deterministic Policy Gradient (DDPG)
• Trust Region Policy Optimization (TRPO)
• Proximal Policy Optimization (PPO)

Frameworks

• Frameworks Overview
• Frameworks and libraries in addition to TensorFlow

TensorFlow

• TensorFlow Overview & Tutorials
• TensorBoard
• TensorFlow.js
• TensorFlow Playground
• TensorFlow Lite
• TensorFlow Serving
• Related...
  • Keras
  • TFLearn
  • Swift

Techniques

Mathematical Background

• Math for Intelligence

Datasets & Information Analysis

• Datasets
• Data Preprocessing & Feature Exploration

Algorithms

• Backpropagation
• Gradient Boosting Algorithms
• Dimensional Reduction Algorithms
• Repositories & Other Algorithms

Bag of Tricks

• Activation Functions
• Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking
• Optimizers
  • Backpropagation
  • Gradient Descent Optimization & Challenges
  • Parameter Initialization
  • Choosing the Right Metric for Evaluating Machine Learning Models
  • Approach to Bias and Variances
• Pooling
• Hyperparameters
• Visualization
• Transfer Learning
• Competitions

Coding

• Javascript
• Python
• Other Coding options
• Jupyter Notebooks

Platforms: Machine Learning as a Service (MLaaS)

• Service Capabilities
• Other Platforms

Amazon AWS

• AWS with TensorFlow
• DeepLens - deep learning enabled video camera
• AWS IoT Button
• AmazonML
• Deep Learning Amazon Machine Image (DLAMI)
• AWS Training and Certification

Google Cloud AI

• Google Cloud AI With TensorFlow
• ML Engine
• Prediction API
• Google Developers Codelabs
• Google AI Experiments
• Cloud Vision API - drag & drop picture on webpage
• Learn from ML experts at Google

Kaggle

• Kaggle Overview
• Kaggle Kernels
• Kaggle Competitions
  • Passenger Screening
• Hands-On Data Science Education

Microsoft Azure

• Azure Process
• Azure with TensorFlow
• ML Studio
• Cognitive Services
• AI School

Research & Development

• Evolution/Genetic Algorithms
• Self Learning Artificial Intelligence - AutoML & World Models
• Explainable Artificial Intelligence
  • Adversarial Threats
• Cybersecurity
• Differentiable Neural Computer (DNC)
• Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)
• 3D Simulation Environments
• Connecting Brains
• Architectures
  • Deep Distributed Q Network Partial Observability
  • Capsule Networks (CapNets)
  • Generative Agents
  • Messaging & Routing
  • Processing Units - CPU, GPU, TPU, VPU
• Other Challenges

Other

• Business-Centric Methodology
• Getting Started - MediaWiki